Merge branch 'aws:master' into master

Author: mizanfiu

.gitignore

@@ -30,5 +30,6 @@ env/
 .vscode/
 **/tmp
 .python-version
-**/_repack_model.py
-**/_repack_script_launcher.sh
+**/_repack_script_launcher.sh
+tests/data/**/_repack_model.py
+tests/data/experiment/sagemaker-dev-1.0.tar.gz

CHANGELOG.md

@@ -1,5 +1,18 @@
 # Changelog
 
+## v2.122.0 (2022-12-14)
+
+### Features
+
+ * Feature Store dataset builder, delete_record, get_record, list_feature_group
+ * Add OSU region to frameworks for DLC
+
+### Bug Fixes and Other Changes
+
+ * the Hyperband support fix for the HPO
+ * unpin packaging version
+ * Remove content type image/jpg from analysis configuration schema
+
 ## v2.121.2 (2022-12-12)
 
 ### Bug Fixes and Other Changes

VERSION

@@ -1 +1 @@
-2.121.3.dev0
+2.122.1.dev0

doc/experiments/index.rst

@@ -0,0 +1,10 @@
+############################
+Amazon SageMaker Experiments
+############################
+
+The SageMaker Python SDK supports to track and organize your machine learning workflow across SageMaker with jobs, such as Processing, Training and Transform, or locally.
+
+.. toctree::
+    :maxdepth: 2
+
+    sagemaker.experiments

doc/experiments/sagemaker.experiments.rst

@@ -0,0 +1,20 @@
+Experiments
+============
+
+Run
+-------------
+
+.. autoclass:: sagemaker.experiments.Run
+    :members:
+
+.. automethod:: sagemaker.experiments.load_run
+
+.. automethod:: sagemaker.experiments.list_runs
+
+.. autoclass:: sagemaker.experiments.SortByType
+    :members:
+    :undoc-members:
+
+.. autoclass:: sagemaker.experiments.SortOrderType
+    :members:
+    :undoc-members:

doc/index.rst

@@ -60,6 +60,16 @@ Orchestrate your SageMaker training and inference workflows with Airflow and Kub
     workflows/index
 
 
+****************************
+Amazon SageMaker Experiments
+****************************
+You can use Amazon SageMaker Experiments to track machine learning experiments.
+
+.. toctree::
+    :maxdepth: 2
+
+    experiments/index
+
 *************************
 Amazon SageMaker Debugger
 *************************

requirements/extras/test_requirements.txt

@@ -20,3 +20,4 @@ requests==2.27.1
 sagemaker-experiments==0.1.35
 Jinja2==3.0.3
 pandas>=1.3.5,<1.5
+scikit-learn==1.0.2

setup.py

@@ -48,7 +48,7 @@ def read_requirements(filename):
 # Declare minimal set for installation
 required_packages = [
     "attrs>=20.3.0,<23",
-    "boto3>=1.26.20,<2.0",
+    "boto3>=1.26.28,<2.0",
     "google-pasta",
     "numpy>=1.9.0,<2.0",
     "protobuf>=3.1,<4.0",

src/sagemaker/amazon/amazon_estimator.py

@@ -27,7 +27,7 @@
 from sagemaker.deprecations import renamed_warning
 from sagemaker.estimator import EstimatorBase, _TrainingJob
 from sagemaker.inputs import FileSystemInput, TrainingInput
-from sagemaker.utils import sagemaker_timestamp
+from sagemaker.utils import sagemaker_timestamp, check_and_get_run_experiment_config
 from sagemaker.workflow.entities import PipelineVariable
 from sagemaker.workflow.pipeline_context import runnable_by_pipeline
 from sagemaker.workflow import is_pipeline_variable
@@ -242,8 +242,8 @@ def fit(
                 generates a default job name, based on the training image name
                 and current timestamp.
             experiment_config (dict[str, str]): Experiment management configuration.
-                Optionally, the dict can contain three keys:
-                'ExperimentName', 'TrialName', and 'TrialComponentDisplayName'.
+                Optionally, the dict can contain four keys:
+                'ExperimentName', 'TrialName', 'TrialComponentDisplayName' and 'RunName'.
                 The behavior of setting these keys is as follows:
                 * If `ExperimentName` is supplied but `TrialName` is not a Trial will be
                 automatically created and the job's Trial Component associated with the Trial.
@@ -255,6 +255,7 @@ def fit(
         """
         self._prepare_for_training(records, job_name=job_name, mini_batch_size=mini_batch_size)
 
+        experiment_config = check_and_get_run_experiment_config(experiment_config)
         self.latest_training_job = _TrainingJob.start_new(
             self, records, experiment_config=experiment_config
         )

src/sagemaker/apiutils/_base_types.py

@@ -173,8 +173,10 @@ def _search(
                 search_items = search_method_response.get("Results", [])
                 next_token = search_method_response.get(boto_next_token_name)
                 for item in search_items:
-                    if cls.__name__ in item:
-                        yield search_item_factory(item[cls.__name__])
+                    # _TrialComponent class in experiments module is not public currently
+                    class_name = cls.__name__.lstrip("_")
+                    if class_name in item:
+                        yield search_item_factory(item[class_name])
                 if not next_token:
                     break
         except StopIteration: